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Start Over Descriptor "classification and regression tree" Publisher springer berlin heidelberg
25 results on '"classification and regression tree"'

9. Causal Analysis and Data Mining of Well Stimulation Data Using Classification and Regression Tree with Enhancements

Author

Bhattacharya, Srimoyee, Maučec, Marko, Yarus, Jeffrey, Fulton, Dwight, Orth, Jon, Singh, Ajay, Blondel, Philippe, Series editor, Reitner, Joachim, Series editor, Stüwe, Kurt, Series editor, Trauth, Martin H., Series editor, Yuen, David A., Series editor, Pardo-Igúzquiza, Eulogio, editor, Guardiola-Albert, Carolina, editor, Heredia, Javier, editor, Moreno-Merino, Luis, editor, Durán, Juan José, editor, and Vargas-Guzmán, Jose Antonio, editor

Published
2014
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11. Rule Extraction from DEWNN to Solve Classification and Regression Problems

Author

Naveen, Nekuri, Ravi, Vadlamani, Rao, Chillarige Raghavendra, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Panigrahi, Bijaya Ketan, editor, Das, Swagatam, editor, Suganthan, Ponnuthurai Nagaratnam, editor, and Nanda, Pradipta Kumar, editor

Published
2012
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13. A Hybrid FMM-CART Model for Fault Detection and Diagnosis of Induction Motors

Author

Seera, Manjeevan, Lim, CheePeng, Ishak, Dahaman, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Lu, Bao-Liang, editor, Zhang, Liqing, editor, and Kwok, James, editor

Published
2011
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14. Toward Non-security Failures as a Predictor of Security Faults and Failures

Author

Gegick, Michael, Rotella, Pete, Williams, Laurie, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Massacci, Fabio, editor, Redwine, Samuel T., Jr., editor, and Zannone, Nicola, editor

Published
2009
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17. Mining Learners’ Behavior in Accessing Web-Based Interface

Author

Lee, Man Wai, Chen, Sherry Y., Liu, Xiaohui, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hui, Kin-chuen, editor, Pan, Zhigeng, editor, Chung, Ronald Chi-kit, editor, Wang, Charlie C. L., editor, Jin, Xiaogang, editor, Göbel, Stefan, editor, and Li, Eric C.-L., editor

Published
2007
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24. Mining Learners’ Behavior in Accessing Web-Based Interface

Author

Xiaohui Liu, Sherry Y. Chen, Man Wai Lee, and Hui, KC

Subjects
Higher education, Multimedia, Classification and regression tree, business.industry, Computer science, Interface (Java), Educational technology, Usability, computer.software_genre, Web-based learning, World Wide Web, Web based interface, Web based learning, Web-based interface, business, Data mining, computer, Learning behavior, Cognitive styles, Cognitive style
Abstract

Web-based technology has already been adopted as a tool to support teaching and learning in higher education. One criterion affecting the usability of such a technology is the design of web-based interface (WBI) within web-based learning programs. How different users access the WBIs has been investigated by several studies, which mainly analyze the collected data using statistical methods. In this paper, we propose to analyze users’ learning behavior using Data Mining (DM) techniques. Findings in our study show that learners with different cognitive styles seem to have various learning preferences, and DM is an efficient tool to analyze the behavior of different cognitive style groups.

Published
2007

25. Multistream Dynamic Bayesian Network for Meeting Segmentation

Author

Steve Renals, Alfred Dielmann, Bengio, Samy, and Bourlard, Hervé

Subjects
Microphone array, classification and regression tree, business.industry, Computer science, Microphone, speech, Speech recognition, Word error rate, Bayesian network, consonant duration, Pattern recognition, Bayesian belief network, Set (abstract data type), sums-of-products, Feature (machine learning), Segmentation, Artificial intelligence, business, Dynamic Bayesian network
Abstract

Consonant duration is influenced by a number of linguistic factors such as the consonant s identity, within-word position, stress level of the previous and following vowels, phrasal position of the word containing the target consonant, its syllabic position, identity of the previous and following segments. In our work, consonant duration is predicted from a Bayesian belief network (BN) consisting of discrete nodes for the linguistic factors and a single continuous node for the consonant s duration. Interactions between factors are represented as conditional dependency arcs in this graphical model. Given the parameters of the belief network, the duration of each consonant in the test set is then predicted as the value with the maximum probability. We compare the results of the belief network model with those of sums-of-products (SoP) and classification and regression tree (CART) models using the same data. In terms of RMS error, our BN model performs better than both CART and SoP models. In terms of the correlation coefficient, our BN model performs better than SoP model, and no worse than CART model. In addition, the Bayesian model reliably predicts consonant duration in cases of missing or hidden linguistic factors.

Published
2005

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